A Neural Network for Hyperspectral Image Denoising by Combining Spatial–Spectral Information
Remote Sensing, 2023 Hyperspectral imaging often suffers from various types of noise, including sensor non-uniformity and atmospheric disturbances. Removing multiple types of complex noise in hyperspectral images (HSIs) while preserving high fidelity in spectral dimensions ...
Xiaoying Lian +6 more
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Bayesian Approach in a Learning-Based Hyperspectral Image Denoising Framework
IEEE Access, 2021 Hyperspectral images (HSI) are corrupted by a combination of Gaussian and impulse noise. Successful denoising of HSI data increases the accuracy of high-level vision operations like classification, target tracking and land-cover problem. On the one hand,
Hazique Aetesam +2 more
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Hyperspectral Image Denoising via Nonlocal Spectral Sparse Subspace Representation
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023 Hyperspectral image (HSI) denoising based on nonlocal subspace representation has attracted a lot of attention recently. However, most of the existing works mainly focus on refining the representation coefficient images (RCIs) using certain nonlocal ...
Hailin Wang +5 more
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Denoising and Destriping Hyperspectral Images Using Double Graph Laplacian Regularizers
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023 This article proposes a novel hyperspectral image (HSI) denoising and destriping method based on graph signal processing that fully exploits the HSI properties.
Fang Yang, Xin Chen, Zhi Zhang, Li Chai
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Effective feature extraction and data reduction with hyperspectral imaging in remote sensing
, 2014 Although PCA has been widely used for feature extraction and data reduction, it suffers from three main drawbacks: high computational cost, large memory requirement and low efficacy in processing large datasets such as HSI.
Zabalza, Jaime +3 more
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Multitask Sparse Neural Network for Hyperspectral Image Denoising
, 2022 Data-driven deep learning (DL)-based methods directly learn the nonlinear mapping between noisy hyperspectral images (HSIs) and corresponding clean ones.
Xiong, F, Zhou, J, Ye, M, Lu, J, Qian, Y
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Hyperspectral Image Denoising Combining Group Sparse and Representative Coefficient Bidirectional Spatial Spectral Total Variation [PDF]
Jisuanji kexueHyperspectral image denoising is a fundamental problem in remote sensing field,which is an important step of preprocessing.Denoising method based on total variation of representative coefficients is widely used in hyperspectral image(HSI) denoising ...
SI Weina, YE Jun, JIANG Bin
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Hyperspectral Image Restoration with Self-supervised Learning: A Two-stage Training Approach
, 2021 Hyperspectral image (HSI) denoising is a crucial preprocessing task to improve the performance of the subsequent HSI interpretation and applications.
Chen, L, Zhou, J, Zhu, H, Qian, Y
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Remote Sensing, 2019 A hyperspectral image (HSI) contains abundant spatial and spectral information, but it is always corrupted by various noises, especially Gaussian noise.
Xiangyang Kong +3 more
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Artificial intelligence‐powered plant phenomics: Progress, challenges, and opportunities
The Plant Phenome Journal, Volume 9, Issue 1, December 2026.Abstract Artificial intelligence (AI), a key driver of the Fourth Industrial Revolution, is being rapidly integrated into plant phenomics to automate sensing, accelerate data analysis, and support decision‐making in phenomic prediction and genomic selection.
Xu Wang +12 more
wiley +1 more source